This disclosure relates generally to online systems, and more specifically to modifying one or more machine learned models used by an online system.
An online system, such as a social networking system, allows its users to connect to and communicate with other online system users. Users may create profiles on an online system that are tied to their identities and include information about the users, such as interests and demographic information. The users may be individuals or entities such as corporations or charities. An online system receives content from various sources, such as users, and selects content items from the received content for presentation to the user. Interactions between the user and the selected content items are maintained by the online system and communicated to other online system users to facilitate interaction between users and the online system.
When selecting content items for presentation to a user, an online system may use one or more machine learned models that receive various features and select one or more content items based on the received features. For example, a machine learned model associates values with different types of user interactions with a content item and determines probabilities of a user performing different types of interactions based on information associated with the user by the online system, such as prior interactions performed by the user, and information associated with the content item. In this example, the machine learned model generates a value associated with the content item, and the online system determines whether to present the content item to the user based on the determined value.
An online system may use multiple machine learned models to select different types of content items for presentation to a user. Different machine learned models may be based on different features associated with the user or with content items. Because of the large number of features used by different machine learned models, modifying features used by one or more of the machine learned models or selecting features used by different machine learned models in conventional online systems is cumbersome and time-consuming.